Information processing apparatus, method for processing information, and program

ABSTRACT

An information processing apparatus includes a specific-evaluation information acquisition unit that acquires an evaluation of a predetermined content item as a specific evaluation, the evaluation having been input by a user in accordance with an ordinal scale; a language-evaluation information extraction unit that acquires a language evaluation from language information regarding an evaluation sentence in which an evaluation of the predetermined content item is expressed in a language, the evaluation sentence having been input by the user; and a recommendation unit that recommends a content item that matches the user&#39;s preference in accordance with whether the specific evaluation is a positive or negative evaluation and whether the language evaluation is a positive or negative evaluation.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an information processing apparatus, amethod for processing information, and a program. More specifically, thepresent invention relates to an information processing apparatus, amethod for processing information, and a program that make it possibleto recommend content items that match preference of a user with higheraccuracy.

2. Description of the Related Art

Typical methods for evaluating content items include a method using atwo-class ordinal scale such as “like” and “dislike” and a method usinga five-class ordinal scale such as a five star system.

However, these evaluation methods do not make it possible to recognizewhich aspect of a content item the user gives a positive evaluation to.Moreover, the evaluation methods do not make it possible to recognizeevaluations given to aspects of the content item. An example of theevaluations is “I like this aspect of the content item but I do not likethat aspect of the content item”.

On the other hand, a technology for analyzing content of a sentencewritten in a natural language has been recently designed (for example,see N. Kobayashi, “Opinion Mining from Web Documents: Extraction andStructurization”, Transaction of The Japanese Society for ArtificialIntelligence, vol. 22, no. 2, 2007, pp. 227 to 238). Thus, if thistechnology is used, an evaluation of a content item can be recognizedfrom a sentence input by the user.

For example, it can be recognized from the sentence “I like the melodyof tune A” input by the user that the user gives a high evaluation“like” to the aspect “melody” of a content item “tune A”.

SUMMARY OF THE INVENTION

However, such a technology has not been fully developed. Moreover,sentences written in a natural language may have ambiguity unique to thelanguage and certain information unique to the language may be omittedfrom those sentences. Thus, such a technology may often have a problemregarding accuracy. Therefore, it may be unlikely that content itemsthat match the user's preference with high accuracy can be recommendedby simply using an evaluation recognized by this technology as theuser's preference.

It is desirable to make it possible to recommend content items thatmatch the user's preference with higher accuracy.

An information processing apparatus according to an embodiment of thepresent invention is an information processing apparatus that includesspecific-evaluation information acquisition means for acquiring anevaluation of a predetermined content item as a specific evaluation, theevaluation having been input by a user in accordance with an ordinalscale; language-evaluation information extraction means for acquiring alanguage evaluation from language information regarding an evaluationsentence in which an evaluation of the predetermined content item isexpressed in a language, the evaluation sentence having been input bythe user; and recommendation means for recommending a content item thatmatches the user's preference in accordance with whether the specificevaluation is a positive or negative evaluation and whether the languageevaluation is a positive or negative evaluation.

A method for processing information and a program according to anembodiment of the present invention correspond to an informationprocessing apparatus according to an embodiment of the presentinvention.

According to an embodiment of the present invention, an evaluation of apredetermined content item is acquired as a specific evaluation, theevaluation having been input by a user in accordance with an ordinalscale; a language evaluation is acquired from language informationregarding an evaluation sentence in which an evaluation of thepredetermined content item is expressed in a language, the evaluationsentence having been input by the user; and a content item that matchesthe user's preference is recommended in accordance with whether thespecific evaluation is a positive or negative evaluation and whether thelanguage evaluation is a positive or negative evaluation.

As described above, according to the embodiments of the presentinvention, content items that match the user's preference with higheraccuracy can be recommended.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary structure of an informationprocessing apparatus according to a first embodiment of the presentinvention;

FIG. 2 is a diagram showing an example of feature values of contentitems and final evaluation values of the content items;

FIG. 3 is a flowchart illustrating learning processing performed by theinformation processing apparatus shown in FIG. 1;

FIG. 4 is a flowchart, illustrating recommended-content presentationprocessing performed by the information processing apparatus shown inFIG. 1;

FIG. 5 is a block diagram of an exemplary structure of an informationprocessing apparatus according to a second embodiment of the presentinvention;

FIG. 6 is a diagram of an example of attribute-and-feature-valuecorrespondence information;

FIG. 7 is a diagram of an example of an input screen;

FIG. 8 is a flowchart illustrating similar-content presentationprocessing performed by the information processing apparatus shown inFIG. 5;

FIG. 9 is a block diagram of an exemplary structure of an informationprocessing apparatus according to a third embodiment of the presentinvention;

FIG. 10 is a flowchart illustrating learning processing performed by theinformation processing apparatus shown in FIG. 9; and

FIG. 11 is a block diagram of an example of the structure of hardware ofa computer.

DESCRIPTION OF THE PREFERRED EMBODIMENTS First Embodiment

FIG. 1 is a block diagram of an exemplary structure of an informationprocessing apparatus according to a first embodiment of the presentinvention.

An information processing apparatus 10 shown in FIG. 1 includes aspecific-evaluation information acquisition unit. 11, anevaluation-sentence acquisition unit 12, a language-evaluationinformation extraction unit 13, aspecific-evaluation-and-language-evaluation comparing unit 14, amain-evaluation information determination unit 15, an evaluationcorrection unit 16, a preference learning unit 17, acontent-feature-value storage unit 18, and arecommendation-and-presentation unit 19.

The specific-evaluation information acquisition unit 11 receives anevaluation of a content item, the evaluation having been input by a userin accordance with a five-class ordinal scale such as the five-starsystem. More specifically, the specific-evaluation informationacquisition unit 11 receives an evaluation value input, by the user,which is an evaluation value of “1” representing an evaluation of “onestar”, an evaluation value of “2” representing an evaluation of “twostars”, an evaluation value of “3” representing an evaluation of “threestars”, an evaluation value of “4” representing an evaluation of “fourstars”, or an evaluation value of “5” representing an evaluation of“five stars”. Here, a higher number of stars indicates a higherevaluation.

Such an evaluation value is input, by the user by operating a keyboardor a mouse (both of them not shown). Evaluation values may be normalizedin such a manner that the average of the evaluation values is 0 and thevariance of the evaluation values is 1.

Moreover, the specific-evaluation information acquisition unit 11 treatsthe evaluation value, which has been input and received, as anevaluation value of a specific evaluation. The specific-evaluationinformation acquisition unit 11 determines whether the specificevaluation input by the user is a positive or negative evaluation inaccordance with the evaluation value of the specific evaluation.

For example, when the evaluation value is greater than three, thespecific-evaluation information acquisition unit 11 determines that thespecific evaluation is a positive evaluation. When the evaluation valueis less than or equal to three, the specific-evaluation informationacquisition unit 11 determines that the specific evaluation is anegative evaluation. The specific-evaluation information acquisitionunit 11 supplies the determination result as the specific evaluation tothe specific-evaluation-and-language-evaluation comparing unit 14.

The evaluation-sentence acquisition unit 12 acquires languageinformation regarding an evaluation sentence, in which an evaluation ofa content item is expressed in a language, the evaluation sentencehaving been input by the user by using a microphone or a keyboard (bothof them not shown). More specifically, the evaluation-sentenceacquisition unit 12 acquires the language information by convertingspeech of the evaluation sentence acquired from the microphone by usinga predetermined speech recognition technology. Moreover, theevaluation-sentence acquisition unit 12 acquires language informationregarding an evaluation sentence described in a blog managed by theuser, a blog managed by a person specified by the user, or the like. Theevaluation-sentence acquisition unit 12 supplies the languageinformation regarding the acquired evaluation sentences to thelanguage-evaluation information extraction unit 13.

The language-evaluation information extraction unit 13 extractsevaluation information, which is information regarding an evaluation,from the language information regarding the evaluation sentencessupplied from the evaluation-sentence acquisition unit 12. For example,the language-evaluation information extraction unit 13 extracts, asevaluation information, the name of the content item “tune A” and anevaluation “like” from the language information regarding the evaluationsentence “I like the melody of tune A very much”.

Moreover, the language-evaluation information extraction unit 13determines whether an evaluation of the evaluation sentences is apositive or negative evaluation in accordance with extracted evaluationinformation, and supplies the determination result as a languageevaluation to the specific-evaluation-and-language-evaluation comparingunit 14.

Here, the language-evaluation information extraction unit. 13 performsprocessing on the premise that the evaluation sentence is related to acontent item that is currently being played or whose content iscurrently being viewed (hereinafter referred to as a subject contentitem). Note that when the content item to be evaluated can be specifiedby the evaluation sentence, whether the content item matches the subjectcontent item may be checked. In this case, when the content itemspecified by the evaluation sentence is different from the subjectcontent item, downstream processing is prevented from being performed.

Moreover, when the evaluation sentences supplied from theevaluation-sentence acquisition unit 12 do not include any wordsrepresenting a positive evaluation or any words representing a negativeevaluation, the language-evaluation information extraction unit 13determines that the language evaluation is neither a positive nornegative evaluation. The language-evaluation information extraction unit13 supplies nothing to the specific-evaluation-and-language-evaluationcomparing unit 14, and downstream processing is not performed.

The specific-evaluation-and-language-evaluation comparing unit 14recognizes a relationship between the specific evaluation supplied fromthe specific-evaluation information acquisition unit 11 and the languageevaluation supplied from the language-evaluation information extractionunit 13. For example, when both the specific evaluation and the languageevaluation are positive evaluations, thespecific-evaluation-and-language-evaluation comparing unit 14 recognizesthat the specific evaluation matches the language evaluation, as therelationship between the specific evaluation and the languageevaluation. On the other hand, when the specific evaluation is anegative evaluation and the language evaluation is a positive evaluationor when the specific evaluation is a positive evaluation and thelanguage evaluation is a negative evaluation, thespecific-evaluation-and-language-evaluation comparing unit 14 recognizesthat the specific evaluation does not match the language evaluation, asthe relationship between the specific evaluation and the languageevaluation. The specific-evaluation-and-language-evaluation comparingunit 14 supplies the relationship between the specific evaluation andthe language evaluation to the evaluation correction unit 16.

The main-evaluation information determination unit 15 determines thatone of the specific evaluation and the language evaluation is to be amain evaluation, which is assumed to reflect the user's true evaluation.In the first embodiment, the specific evaluation is predetermined to bethe main evaluation. The main-evaluation information determination unit15 specifies the specific evaluation as the main evaluation in theevaluation correction unit 16.

The evaluation correction unit 16 corrects the evaluation value of thespecific evaluation supplied from the specific-evaluation informationacquisition unit 11, in accordance with the relationship between thespecific evaluation and the language evaluation supplied from thespecific-evaluation-and-language-evaluation comparing unit 14. Theevaluation correction unit 16 supplies the corrected evaluation value ofthe specific evaluation as a final evaluation value to the preferencelearning unit 17.

The preference learning unit 17 performs preference learning by usingthe final evaluation value supplied from the evaluation correction unit16 and a feature value of the subject content item stored in thecontent-feature-value storage unit 18.

As a first method of the preference learning, there is a method forlearning, as preference information, a relational expression expressingthe relationship between a feature value and the final evaluation valueby performing linear regression in which the feature value of a contentitem is treated as an independent variable and the final evaluationvalue is treated as a dependent variable. Here, when the content item isa tune, feature values of the content item include musical or vocalfeatures, or property values such as a genre, keywords, and a mood.

Moreover, as a second method of the preference learning, there is amethod for learning, as preference information, an average vector of acontent group corresponding to the final evaluation value representing apositive evaluation (hereinafter the average vector being referred to asa preference vector), feature values of each of the content items beingrepresented as a vector in the second method. This second method isspecifically described in Japanese Unexamined Patent ApplicationPublication No. 2001-160955.

Here, when the preference learning unit 17 performs preference learningby using the second method, the preference learning unit 17 may create,as negative preference information, an average vector of a content groupcorresponding to the final evaluation value representing a negativeevaluation (hereinafter the average vector being referred to as anegative preference vector), similarly to the final evaluation valuerepresenting a positive evaluation.

The preference learning unit 17 supplies the created preferenceinformation to the recommendation-and-presentation unit 19. Thecontent-feature-value storage unit 18 stores feature values of contentitems.

When the recommendation-and-presentation unit 19 receives the relationalexpression as the preference information from the preference learningunit 17, the recommendation-and-presentation unit 19 calculates, foreach of the content items that the user has not yet evaluated(hereinafter referred to as unvalued content items), a predictedevaluation value of the unvalued content item in accordance with therelational expression and the feature values of the content items storedin the content-feature-value storage unit 18. Moreover, when therecommendation-and-presentation unit 19 receives the preference vectoras the preference information from the preference learning unit 17, therecommendation-and-presentation unit 19 calculates the degree ofsimilarity or the distance between the preference vector and the vectorrepresenting feature values of the unvalued content item.

Then, the recommendation-and-presentation unit 19 treats, for each ofthe unvalued content items, the name of the unvalued content item andthe like as an information item regarding the content item (hereinafterreferred to as a content-related information item), and makes a displayunit (not shown) display, as content-related information items regardingrecommended content items, content-related information items regardingthe unvalued content items in descending order of the predictedevaluation value or the degree of similarity or in ascending order ofthe distance. That is, the recommendation-and-presentation unit. 19recommends content items that match the user's preference and makes thecontent-related information items regarding the content items bedisplayed.

Here, in the information processing apparatus 10, the main evaluationdoes not have to be predetermined, but may be determined in accordancewith the magnitude of a prediction error resulting from the preferencelearning performed by the preference learning unit 17.

In this case, the specific-evaluation information acquisition unit 11supplies an evaluation value whose input has been accepted also to themain-evaluation information determination unit 15. Moreover, when theevaluation of the evaluation sentences is a positive evaluation, thelanguage-evaluation information extraction unit 13 supplies anevaluation value between four and five or equal to four or five to themain-evaluation information determination unit 15. In contrast, when theevaluation of the evaluation sentences is a negative evaluation, thelanguage-evaluation information extraction unit 13 supplies anevaluation value between one and three or equal to one or three to themain-evaluation information determination unit 15.

Here, when the language-evaluation information extraction unit 13 canrecognize the level of language evaluation (hereinafter referred to as alanguage evaluation level) from the evaluation sentences supplied fromthe evaluation-sentence acquisition unit 12, the language-evaluationinformation extraction unit 13 holds the language evaluation 1 and setsthe evaluation value to a value within the above-described range inaccordance with the language evaluation level. For example, thelanguage-evaluation information extraction unit 13 recognizes that thelanguage evaluation level of the evaluation sentence “I like the melodyof tune A very much” is high from “very much” in the evaluationsentence. For example, in a case in which the language evaluation levelis a value in the range from 0 to 1, the language-evaluation informationextraction unit 13 sets the language evaluation value to 0.8 and theevaluation value to 4.8 (=4+0.8×(5−4)).

Furthermore, the preference learning unit 17 performs not onlypreference learning by using the specific evaluation but also preferencelearning by using the language evaluation, and calculates a predictedvalue of the evaluation value of the specific evaluation and a predictedvalue of the evaluation value of the language evaluation regarding thesubject content item.

The main-evaluation information determination unit 15 calculates aprediction error in accordance with the predicted value of theevaluation value of the specific evaluation of the subject content itemsupplied from the preference learning unit 17 and the evaluation valuesupplied from the specific-evaluation information acquisition unit 11.Moreover, the main-evaluation information determination unit 15calculates a prediction error in accordance with the predicted value ofthe evaluation value of the language evaluation of the subject contentitem supplied from the preference learning unit 17 and the evaluationvalue supplied from the language-evaluation information extraction unit13. Then, the main-evaluation information determination unit 15determines that the main evaluation is to be one of the specificevaluation and language evaluation that has a smaller prediction error.

The ordinal scale accepted by the specific-evaluation informationacquisition unit 11 may have any number of classes. For example, whenthe ordinal scale has two classes such as “like” and “dislike”, thespecific-evaluation information acquisition unit 11 receives anevaluation value of “1” representing an evaluation “like” input by theuser or an evaluation value of “−1” representing an evaluation of“dislike” input by the user. When the evaluation value is “1”, thespecific-evaluation information acquisition unit 11 determines that thespecific evaluation is a positive evaluation. When the evaluation valueis “−1”, the specific-evaluation information acquisition unit 11determines that the specific evaluation is a negative evaluation.

Here, the following will describe a case in which the preferencelearning unit 17 performs preference learning by using the second methodand the recommendation-and-presentation unit 19 calculates the degree ofsimilarity regarding the unvalued content items in accordance with thepreference information.

FIG. 2 is a diagram showing an example of feature values of contentitems and final evaluation values of the content items.

As shown in FIG. 2, when the content items are tunes, thecontent-feature-value storage unit 18 stores, for example, the speed,the cheerfulness, and the sound density as feature values of the contentitems.

In the example shown in FIG. 2, the speed of tune A is 35, thecheerfulness of tune A is 50, and the sound density of tune A is 84. Thespeed of tune B is 70, the cheerfulness of tune B is 58, and the sounddensity of tune B is 37. The speed of tune C is 88, the cheerfulness oftune C is 80, and the sound density of tune C is 20. The speed of tune Dis 50, the cheerfulness of tune D is 60, and the sound density of tune Dis 65.

The speed of tune E is 60, the cheerfulness of tune E is 75, and thesound density of tune E is 55. The speed of tune F is 66, thecheerfulness of tune F is 55, and the sound density of tune F is 40. Thespeed of tune G is 38, the cheerfulness of tune G is 20, and the sounddensity of tune G is 63. The speed of tune H is 25, the cheerfulness oftune H is 37, and the sound density of tune H is 42. The speed of tune Iis 73, the cheerfulness of tune I is 59, and the sound density of tune Iis 76.

Tunes A, B, C, D, and E have already been evaluated by the user. Thefinal evaluation value of tune A is 2.0, that of tune B is 3.5, that oftune C is 5.5, that of tune D is 1.5, and that of tune E is 4.0.

In this case, for example, when the preference learning unit 17determines that final evaluation values greater than three are positivefinal evaluation values, an average vector (72.7, 71, 37.3) of tunes B,C, and E is a preference vector.

The recommendation-and-presentation unit 19 calculates the reciprocal,which is 0.057, of the Euclidean distance between the vectorrepresenting feature values of tune F, which is unvalued, and thepreference vector; the reciprocal, which is 0.014, of the Euclideandistance between the vector representing feature values of tune G, whichis unvalued, and the preference vector; the reciprocal, which is 0.017,of the Euclidean distance between the vector representing feature valuesof tune H, which is unvalued, and the preference vector; and thereciprocal, which is 0.025, of the Euclidean distance between the vectorrepresenting feature values of tune I, which is unvalued, and thepreference vector. The recommendation-and-presentation unit 19 sets thedegree of similarity regarding tune F to 0.057, the degree of similarityregarding tune G to 0.014, the degree of similarity regarding tune H to0.017, and the degree of similarity regarding tune I to 0.025. As aresult, content-related information items regarding tunes F to I arepresented to the user in descending order of the degree of similarity,that is, in order of tune F, tune I, tune H, and tune G.

Here, when the preference learning unit 17 creates negative preferenceinformation, the recommendation-and-presentation unit 19 may be allowednot to present content-related information items regarding tunes whoseEuclidean distance to the negative preference information is short, fromamong the unvalued tunes F to I.

FIG. 3 is a flowchart illustrating learning processing performed by theinformation processing apparatus 10 shown in FIG. 1. This learningprocessing starts when, for example, the user inputs a class of afive-class ordinal scale and an evaluation sentence regarding a subjectcontent item.

In step S11, the specific-evaluation information acquisition unit 11receives an evaluation, which is a class of the five-class ordinal scaleinput by the user. In step S12, the specific-evaluation informationacquisition unit 11 treats the evaluation value received in step S11 asthe evaluation value of a specific evaluation, and determines whetherthe specific evaluation input by the user is a positive or negativeevaluation in accordance with the evaluation value of the specificevaluation. Then, the specific-evaluation information acquisition unit11 supplies the determination result as the specific evaluation to thespecific-evaluation-and-language-evaluation comparing unit 14.

In step S13, the evaluation-sentence acquisition unit 12 acquireslanguage information regarding the evaluation sentence input by theuser. In step S14, the language-evaluation information extraction unit13 extracts evaluation information from the language informationregarding the evaluation sentence supplied from the evaluation-sentenceacquisition unit 12.

In step S15, the language-evaluation information extraction unit 13determines whether the evaluation of the evaluation sentence is apositive or negative evaluation in accordance with the evaluationinformation extracted in step S14.

If the language-evaluation information extraction unit 13 determinesthat the evaluation of the evaluation sentence is neither a positive nornegative evaluation in step S15, the procedure ends.

In contrast, if the language-evaluation information extraction unit 13determines that the evaluation of the evaluation sentence is a positiveor negative evaluation in step S15, the language-evaluation informationextraction unit 13 supplies a determination result indicating that theevaluation of the evaluation sentence is a positive or negativeevaluation, as a language evaluation, to thespecific-evaluation-and-language-evaluation comparing unit 14.

In step S16, the specific-evaluation-and-language-evaluation comparingunit 14 recognizes a relationship between the specific evaluationsupplied from the specific-evaluation information acquisition unit 11and the language evaluation supplied from the language-evaluationinformation extraction unit 13. Thespecific-evaluation-and-language-evaluation comparing unit 14 suppliesthe relationship between the specific evaluation and the languageevaluation to the evaluation correction unit 16.

In step S17, the main-evaluation information determination unit 15determines that the main evaluation is to be the specific evaluation,and specifies the specific evaluation as the main evaluation in theevaluation correction unit 16. In step S18, the evaluation correctionunit 16 determines whether the specific evaluation matches the languageevaluation in accordance with the relationship between the specificevaluation and the language evaluation supplied from thespecific-evaluation-and-language-evaluation comparing unit 14.

In step S18, if the evaluation correction unit 16 determines that thespecific evaluation matches the language evaluation, that is, both thespecific evaluation and the language evaluation are positive evaluationsor both of them are negative evaluations, the procedure proceeds to stepS19.

In step S19, the evaluation correction unit 16 corrects the evaluationvalue of the specific evaluation supplied from the specific-evaluationinformation acquisition unit 11 in such a manner that the specificevaluation, which is the main evaluation, is given a heavier weight.

More specifically, for example, when both the specific evaluation andthe language evaluation are positive evaluations, it is assumed thatthere is a high probability that the user gives a positive evaluation tothe subject content item. As a result, the evaluation correction unit 16changes, for example, the evaluation value, which is 4, of the specificevaluation supplied from the specific-evaluation information acquisitionunit 11 to 4.5 by adding 0.5 to the evaluation value.

Here, the evaluation correction unit 16 may change the evaluation valueof the specific evaluation to a constant multiple thereof. Moreover,when the language-evaluation information extraction unit 13 recognizesthe language evaluation level, the evaluation correction unit 16 may setthe magnitude of correction in accordance with a degree corresponding tothe language evaluation level. In this case, for example, when theevaluation sentence includes “very much”, the evaluation correction unit16 sets the magnitude of correction to 0.8, which is larger than anaddition value of 0.5 used when the evaluation sentence does not include“very much”.

Then, the evaluation correction unit 16 supplies the correctedevaluation value as the final evaluation value to the preferencelearning unit 17. Then, the procedure proceeds to step S21.

In contrast, if the evaluation correction unit 16 determines that thespecific evaluation does not match the language evaluation in step S18,that is, one of the specific evaluation and the language evaluation is apositive evaluation and the other is a negative evaluation, theprocedure proceeds to step S20.

In step S20, the evaluation correction unit 16 corrects the specificevaluation in such a manner that the specific evaluation, which is themain evaluation, is given a lighter weight, that is, the specificevaluation is made to be closer to a middle evaluation.

More specifically, for example, when the specific evaluation is anegative evaluation and the language evaluation is a positiveevaluation, it is assumed that although the user gives a rather lowevaluation to the subject content item in terms of the specificevaluation, there is an aspect the user gives a high evaluation to.Thus, for example, the evaluation correction unit 16 changes theevaluation value, which is 2, of the specific evaluation supplied fromthe specific-evaluation information acquisition unit 11 to 2.5, which iscloser to the middle value, which is 3, of an evaluation value range by0.5.

Here, the evaluation correction unit 16 may change the evaluation valueof the specific evaluation to a value which is closer to the middlevalue by a constant multiple of the difference between the evaluationvalue of the specific evaluation and the middle value of the evaluationvalue range. In this case, the evaluation value of the specificevaluation after correction is a value obtained by adding the middlevalue to a constant multiple of the value that is obtained bysubtracting the middle value from the evaluation value of the specificevaluation before the correction. For example, when the evaluation valueof the specific evaluation before correction is 2 and a constant is 0.5,the evaluation value of the specific evaluation after the correction is2.5 (=3+(2−3)×0.5). The constant used here may be preset, or may be setin accordance with the degree corresponding to the language evaluationlevel. When the constant is set in accordance with the languageevaluation level, the specific evaluation is given a lighter weight inaccordance with a degree corresponding to the language evaluation level.

The evaluation correction unit 16 supplies the corrected evaluationvalue as the final evaluation value to the preference learning unit 17.Then, the procedure proceeds to step S21.

In step S21, the preference learning unit 17 performs preferencelearning by using the final evaluation values of evaluated content itemsincluding the final evaluation value of the subject content itemsupplied from the evaluation correction unit 16 and the feature valuesof evaluated content items including the subject content item stored inthe content-feature-value storage unit 18. Then, the procedure ends.

FIG. 4 is a flowchart illustrating recommended-content presentationprocessing performed by the information processing apparatus 10. Thisrecommended-content presentation processing starts when, for example,the user commands presentation of recommended content items.

In step S31, for each of unvalued content items stored in thecontent-feature-value storage unit 18, therecommendation-and-presentation unit 19 calculates the degree ofsimilarity between the preference vector created by the preferencelearning unit 17 and the vector representing feature values of theunvalued content item.

In step S32, the recommendation-and-presentation unit 19 makes a displayunit, which is not shown, display content-related information itemsregarding the unvalued content items, as content-related informationitems regarding recommended content items, in descending order of thedegree of similarity. Then, the procedure ends.

As described above, the information processing apparatus 10 recommendsand presents content items in accordance with the relationship betweenthe specific evaluation and the language evaluation. Thus, theinformation processing apparatus 10 can recommend and present contentitems that match the user's preference with higher accuracy than a casein which content items are recommended in accordance with one of thespecific evaluation and the language evaluation.

Second Embodiment

FIG. 5 is a block diagram of an exemplary structure of an informationprocessing apparatus according to a second embodiment of the presentinvention.

In the structure shown in FIG. 5, components the same as those indicatedin the structure shown in FIG. 1 will be denoted by the same referencenumerals. Redundant explanation will be omitted as necessary.

The structure of an information processing apparatus 30 shown in FIG. 5and the structure shown in FIG. 1 differ in that the informationprocessing apparatus 30 includes a language-evaluation informationextraction unit 31, a specific-evaluation-and-language-evaluationcomparing unit 32, a preference learning unit 36, and arecommendation-and-presentation unit 35 instead of thelanguage-evaluation information extraction unit 13, thespecific-evaluation-and-language-evaluation comparing unit 14, thepreference learning unit 17, and the recommendation-and-presentationunit 19. The structure of the information processing apparatus 30 shownin FIG. 5 and the structure shown in FIG. 1 also differ in that theinformation processing apparatus 30 does not include the main-evaluationinformation determination unit 15 or the evaluation correction unit 16,but includes an attribute-and-feature-value correspondence storage unit33 and a language-evaluation feature-value determination unit 34.

The information processing apparatus 30 determines a feature valuepreferred by the user from among feature values of a subject contentitem in accordance with a specific evaluation, a language evaluation,and feature values of content items corresponding to evaluationattributes of the language evaluation. Then, the information processingapparatus 30 presents content-related information items regardingcontent items whose feature value is similar to the determined featurevalue, as content-related information items regarding similar contentitems, to the user.

Here, an evaluation attribute represents an aspect of a content item,and is a word that is appropriate for representing a feature value ofthe content item. For example, when the subject content item is a tune,evaluation attributes are the melody, the vocal quality, the rhythm, andthe like. When the subject content item is a movie, evaluationattributes are the story, the atmosphere, the background music (BGM),and the like.

The language-evaluation information extraction unit 31 of theinformation processing apparatus 30 extracts evaluation informationincluding an evaluation attribute from the language informationregarding the evaluation sentence supplied from the evaluation-sentenceacquisition unit 12, and holds the evaluation information. For example,the language-evaluation information extraction unit 31 extracts, as theevaluation information, the name of the content item “tune A”, anevaluation “like”, and an evaluation attribute of “melody” from thelanguage information regarding the evaluation sentence “I like themelody of tune A very much”.

Here, when any evaluation attributes are not extracted from theevaluation sentence, for example, when the evaluation sentence is “Ilike tune A” and no evaluation attribute is included, thelanguage-evaluation information extraction unit 31 makes the displayunit, not shown, display an input screen (which will be described withreference to FIG. 7) that allows the user to input a feature value.Then, the language-evaluation information extraction unit 31 acquires afeature value from the information input by the user through the inputscreen, and holds the feature value. This feature value is supplied as alanguage feature value to the recommendation-and-presentation unit 35via the language-evaluation feature-value determination unit 34.

Moreover, similarly to the language-evaluation information extractionunit 13 shown in FIG. 1, the language-evaluation information extractionunit 31 determines whether the evaluation of the evaluation sentence isa positive or negative evaluation in accordance with the extractedevaluation information. The language-evaluation information extractionunit 31 supplies the determination result as the language evaluation tothe specific-evaluation-and-language-evaluation comparing unit 32.

Furthermore, similarly to the language-evaluation information extractionunit 13, when the evaluation sentence supplied from theevaluation-sentence acquisition unit 12 is neither a positive nornegative evaluation, the language-evaluation information extraction unit31 supplies nothing to the specific-evaluation-and-language-evaluationcomparing unit 32 and downstream processing is not performed.

Similarly to the specific-evaluation-and-language-evaluation comparingunit 14 shown in FIG. 1, the specific-evaluation-and-language-evaluationcomparing unit 32 recognizes a relationship between the specificevaluation supplied from the specific-evaluation information acquisitionunit 11 and the language evaluation supplied from thelanguage-evaluation information extraction unit 31.

The specific-evaluation-and-language-evaluation comparing unit 32supplies the relationship between the specific evaluation and thelanguage evaluation to the preference learning unit 36. Moreover, thespecific-evaluation-and-language-evaluation comparing unit 32 suppliesthe specific evaluation and the language evaluation to therecommendation-and-presentation unit 35.

The attribute-and-feature-value correspondence storage unit 33 stores,as information regarding the correspondences between attributes andfeature values (hereinafter referred to as attribute-and-feature-valuecorrespondence information), a table in which predetermined evaluationattributes are related to feature values of content items.

The language-evaluation feature-value determination unit 34 determinesthat a language-evaluation feature value is to be a feature valuecorresponding to an evaluation attribute, which is a word similar to theevaluation attribute extracted by the language-evaluation informationextraction unit 31, from among the evaluation attributes included in theattribute-and-feature-value correspondence information stored in theattribute-and-feature-value correspondence storage unit 33. Here, thedetermination regarding similarity is performed by utilizing a thesaurusor the like. Moreover, the language-evaluation feature-valuedetermination unit 34 supplies the language-evaluation feature value tothe recommendation-and-presentation unit 35.

The recommendation-and-presentation unit 35 makes the display unit, notshown, display content-related information items regarding similarcontent items or recommended content items.

More specifically, the recommendation-and-presentation unit 35determines one or more feature values (hereinafter referred to as asubject feature value or subject feature values) that are to be used tocalculate, for each of recommendation-candidate content items, thedegree of similarity or the distance between the subject content itemand the recommendation-candidate content item, in accordance with thespecific evaluation and language evaluation supplied from thespecific-evaluation-and-language-evaluation comparing unit 32 and thelanguage-evaluation feature value supplied from the language-evaluationfeature-value determination unit 34. The recommendation-and-presentationunit 35 calculates the degree of similarity or the distance between thesubject content item and the recommendation-candidate content item inaccordance with the subject feature value or values of the subjectcontent item and the subject feature value or values of therecommendation-candidate content item stored in thecontent-feature-value storage unit 18. Therecommendation-and-presentation unit 35 makes the display unit, notshown, display content-related information items regarding therecommendation-candidate content items as content-related informationitems regarding similar content items in descending order of the degreeof similarity or ascending order of the distance. That is, content itemsthat match the user's preference are recommended to the user as similarcontent items.

Here, when the recommendation-and-presentation unit 35 makes thecontent-related information items regarding the similar content items bedisplayed, the recommendation-and-presentation unit 35 may also make afeature value other than the subject feature value or values bedisplayed in addition to the content-related information items. In thiscase, for example, when the name of the subject content item is “tune I”and the feature value other than the subject feature value or values is“sound density”, a message such as “Content item that is similar to tuneI in terms of feature values other than the sound density is XX” isdisplayed. Here, “XX” is the name of a similar content item.

Moreover, similarly to the recommendation-and-presentation unit 19 shownin FIG. 1, the recommendation-and-presentation unit 35 calculates, foreach of unvalued content items, the degree of similarity between thepreference vector supplied as preference information from the preferencelearning unit 36 and the vector representing feature values of theunvalued content item. Then, the recommendation-and-presentation unit 35makes the display unit, not shown, display content-related informationitems regarding the unvalued content items as content-relatedinformation items regarding recommended content items in descendingorder of the degree of similarity. Thus, the content-related informationitems regarding content items that match the user's preference arepresented to the user as content-related information items regarding therecommended content items.

The preference learning unit 36 performs processing similar to thatperformed by the main-evaluation information determination unit 15, theevaluation correction unit 16, and the preference learning unit 17 shownin FIG. 1.

Here, the information processing apparatus 30 does not have to includethe preference learning unit 36. In this case, therecommendation-and-presentation unit 35 has just a function fordisplaying content-related information items regarding similar contentitems.

The following will describe a case in which therecommendation-and-presentation unit 35 calculates the degree ofsimilarity.

FIG. 6 is a diagram of an example of attribute-and-feature-valuecorrespondence information.

As shown in FIG. 6, the attribute-and-feature-value correspondenceinformation has a table format in which evaluation attributes arerelated to feature values.

In the attribute-and-feature-value correspondence information shown inFIG. 6, evaluation attributes such as “tempo”, “speedy”, and “speed” arerelated to a feature value “speed”. Moreover, evaluation attributes suchas “cheerful” and “festive” are related to a feature value“cheerfulness”. Furthermore, an evaluation feature such as “very deep”is related to a feature value “sound density”.

In this case, for example, when the language-evaluation informationextraction unit 31 extracts the evaluation attribute “tempo”, thelanguage-evaluation feature-value determination unit 34 determines thatthe language-evaluation feature value is to be the feature value“speed”, to which the evaluation attribute “tempo” is related in theattribute-and-feature-value correspondence information.

FIG. 7 is a diagram of an example of an input screen.

On the input screen shown in FIG. 7, the message “which aspect of tune Ado you like?”, a list of feature values, and an OK button are displayed.Moreover, for each of the feature values in the list, a check box isdisplayed to the left of the feature value. When a feature value isselected, a checkmark is placed in the corresponding check box.

As the list of feature values displayed on the input screen, featurevalues corresponding to the type of subject content item (for example, atune, a video, and the like) are displayed from among the feature valuesregistered in the attribute-and-feature-value correspondence storageunit 33. Moreover, “all” representing all the displayed feature valuesand “others” representing feature values that have not been registeredin the attribute-and-feature-value correspondence storage unit 33 arealso displayed.

In the example shown in FIG. 7, the attribute-and-feature-valuecorrespondence information shown in FIG. 6 is stored in theattribute-and-feature-value correspondence storage unit 33. In the listof feature values, the feature values “speed”, “cheerfulness”, and“sound density” as well as “all” and “others” are displayed.

When the input screen as shown in FIG. 7 is displayed, the user operatesa keyboard or a mouse, not shown, and places a checkmark in a check boxcorresponding to a feature value, which is an aspect of tune A that theuser likes, from among the feature values “speed”, “cheerfulness”,“sound density”, “all”, and “others” displayed in the list of featurevalues. Here, tune A is the subject content item. Then, the useroperates the ON button. As a result, the language-evaluation informationextraction unit 31 acquires the feature value. Here, the user may placea checkmark in a plurality of check boxes.

Moreover, when any evaluation attributes are not extracted from theevaluation sentence, a text area that allows the user to arbitrarilyinput evaluation attributes may be displayed instead of the input screenshown in FIG. 7. In this case, the language-evaluation informationextraction unit 31 acquires an evaluation attribute input in the textarea by the user and supplies the evaluation attribute to thelanguage-evaluation feature-value determination unit 34. Thelanguage-evaluation feature-value determination unit 34 treats theevaluation attribute in a similar way in which the language-evaluationfeature-value determination unit 34 treats evaluation attributesextracted from the evaluation sentence.

FIG. 8 is a flowchart illustrating similar-content presentationprocessing performed by the information processing apparatus 30 shown inFIG. 5. This similar-content presentation processing starts when, forexample, presentation of similar content items is instructed.

Processing in steps S51 to S53 is similar to processing in steps S11 toS13 shown in FIG. 3. Thus, the description thereof is omitted.

In step S54, the language-evaluation information extraction unit 31extracts evaluation information including an evaluation attribute fromthe language information regarding the evaluation sentence supplied fromthe evaluation-sentence acquisition unit 12.

In step S55, the language-evaluation information extraction unit 31determines whether the evaluation of the evaluation sentence is apositive or negative evaluation in accordance with the evaluationinformation extracted in step S54.

If the language-evaluation information extraction unit 31 determinesthat the evaluation of the evaluation sentence is neither a positive nornegative evaluation in step S55, the procedure ends.

In contrast, if the language-evaluation information extraction unit 31determines that the evaluation of the evaluation sentence is a positiveor negative evaluation in step S55, the language-evaluation informationextraction unit 31 supplies, as a language evaluation, a determinationresult indicating that the evaluation of the evaluation sentence is apositive or negative evaluation to thespecific-evaluation-and-language-evaluation comparing unit 32.

In step S56, the specific-evaluation-and-language-evaluation comparingunit 32 recognizes the relationship between the specific evaluationsupplied from the specific-evaluation information acquisition unit 11and the language evaluation supplied from the language-evaluationinformation extraction unit 31. Then, thespecific-evaluation-and-language-evaluation comparing unit 32 suppliesthe relationship between the specific evaluation and the languageevaluation to the preference learning unit 36. Moreover, thespecific-evaluation-and-language-evaluation comparing unit 32 suppliesthe specific evaluation and the language evaluation to therecommendation-and-presentation unit 35.

In step S57, the preference learning unit 36 performs preferencelearning by performing processing similar to processing in steps S17 toS21 shown in FIG. 3.

In step S58, the language-evaluation feature-value determination unit 34determines that the language-evaluation feature value is to be a featurevalue corresponding to an evaluation attribute, which is a word similarto the evaluation attribute extracted by the language-evaluationinformation extraction unit 31, from among the evaluation attributes inthe attribute-and-feature-value correspondence information stored in theattribute-and-feature-value correspondence storage unit 33. Then, thelanguage-evaluation feature-value determination unit 34 supplies thelanguage-evaluation feature value to the recommendation-and-presentationunit 35.

In step S59, the recommendation-and-presentation unit 35 determineswhether the specific evaluation is a positive evaluation and thelanguage evaluation is a negative evaluation in accordance with thespecific evaluation and language evaluation supplied from thespecific-evaluation-and-language-evaluation comparing unit 32.

If the recommendation-and-presentation unit 35 determines that thespecific evaluation is a positive evaluation and the language evaluationis a negative evaluation in step S59, the procedure proceeds to stepS60.

In step S60, the recommendation-and-presentation unit 35 selects featurevalues other than the language-evaluation feature value as the subjectfeature values and calculates, for each of recommendation-candidatecontent items, the degree of similarity between the subject content itemand the recommendation-candidate content item.

More specifically, when the specific evaluation is a positive evaluationand the language evaluation is a negative evaluation, it is assumed thatthe user has a positive impression of the subject content item in termsof feature values other than the language-evaluation feature value.Thus, the recommendation-and-presentation unit 35 determines that thesubject feature values are to be the feature values other than thelanguage-evaluation feature value and calculates, for each of therecommendation-candidate content items, the degree of similarity betweenthe subject content item and the recommendation-candidate content item.

For example, when the subject content item is tune I shown in FIG. 2 andthe language-evaluation feature value is “sound density”, therecommendation-and-presentation unit 35 calculates, as the degree ofsimilarity, the reciprocal of the Euclidean distance between the vectorrepresenting feature values of tune I other than the sound density oftune I and the vector representing feature values of tune F other thanthe sound density of tune F, the reciprocal of the Euclidean distancebetween the vector representing the feature values of tune I other thanthe sound density of tune I and the vector representing feature valuesof tune G other than the sound density of tune G, and the reciprocal ofthe Euclidean distance between the vector representing the featurevalues of tune I other than the sound density of tune I and the vectorrepresenting feature values of tune H other than the sound density oftune H, tunes F to H serving as recommendation-candidate content items.As a result, the degree of similarity regarding tune F is 0.12, thedegree of similarity regarding tune G is 0.017, and the degree ofsimilarity regarding tune H is 0.019.

Here, instead of not utilizing the language-evaluation feature value tocalculate the degree of similarity, the recommendation-and-presentationunit 35 may correct the language-evaluation feature value of the subjectcontent item and utilize the corrected evaluation feature value tocalculate the degree of similarity.

In this case, for example, when the average value of the sound densitiesof all the content items is 50, the recommendation-and-presentation unit35 corrects the sound density, which is 76, of tune I to 24, which is avalue symmetric to the original value of the sound density with respectto the average value. Then, for each of the recommendation-candidatecontent items, the recommendation-and-presentation unit 35 calculates,as the degree of similarity, the reciprocal of the Euclidean distancebetween the vector (73, 59, 24) representing the feature values of tuneI after correction and the vector representing the feature values of therecommendation-candidate content item.

Here, the recommendation-and-presentation unit 35 may correct thelanguage-evaluation feature value by using the preference vector. Inthis case, for example, when the preference vector is (72.7, 71, 37.3),the recommendation-and-presentation unit 35 corrects the sound densityof tune I to the sound density, which is 37.3, represented by thepreference vector. Then, for each of the recommendation-candidatecontent items, the recommendation-and-presentation unit 35 calculates,as the degree of similarity, the reciprocal of the Euclidean distancebetween the vector (73, 59, 37.3) representing the feature values oftune I after correction and the vector representing the feature valuesof the recommendation-candidate content item.

In contrast, if the recommendation-and-presentation unit 35 determinesthat the specific evaluation is not a positive evaluation or thelanguage evaluation is not a negative evaluation in step S59, theprocedure proceeds to step S61. In step S61, therecommendation-and-presentation unit 35 determines whether the specificevaluation is a negative evaluation and the language evaluation is apositive evaluation in accordance with the specific evaluation andlanguage evaluation supplied from thespecific-evaluation-and-language-evaluation comparing unit 32.

If the recommendation-and-presentation unit 35 determines that thespecific evaluation is a negative evaluation and the language evaluationis a positive evaluation in step S61, the procedure proceeds to stepS62. In step S62, the recommendation-and-presentation unit 35 selectsthe language-evaluation feature value as the subject feature value, andcalculates, for each of the recommendation-candidate content items, thedegree of similarity between the subject content item and therecommendation-candidate content item.

More specifically, when the specific evaluation is a negative evaluationand the language evaluation is a positive evaluation, it is assumed thatthe user has a positive impression of the subject content item only interms of the language-evaluation feature value. Thus, therecommendation-and-presentation unit 35 determines only thelanguage-evaluation feature value to be the subject feature value andcalculates, for each of the recommendation-candidate content items, thedegree of similarity between the subject content item and therecommendation-candidate content item.

For example, when the subject content item is tune G shown in FIG. 2 andthe language-evaluation feature value is “sound density”, therecommendation-and-presentation unit 35 calculates, as the degree ofsimilarity, the reciprocal of the Euclidean distance between the vectorrepresenting the sound density of tune G and the vector representing thesound density of tune F, the reciprocal of the Euclidean distancebetween the vector representing the sound density of tune G and thevector representing the sound density of tune H, and the reciprocal ofthe Euclidean distance between the vector representing the sound densityof tune G and the vector representing the sound density of tune I, tunesF, H, and I serving as recommendation-candidate content items. As aresult, the degree of similarity regarding tune F is 0.043, the degreeof similarity regarding tune H is 0.048, and the degree of similarityregarding tune I is 0.077.

Here, instead of utilizing only the language-evaluation feature value tocalculate the degree of similarity, the recommendation-and-presentationunit 35 may correct feature values of the subject content item otherthan the language-evaluation feature value of the subject content itemand utilize the corrected feature values to calculate the degree ofsimilarity, similarly to the case in which the specific evaluation is apositive evaluation and the language evaluation is a negativeevaluation.

After processing in step S60 or S62 is performed, in step S63, therecommendation-and-presentation unit 35 makes content-relatedinformation items regarding the recommendation-candidate content itemsbe displayed as content-related information items regarding similarcontent items in descending order of the degree of similarity calculatedin step S60 or S62. Then, the procedure ends.

Moreover, if the recommendation-and-presentation unit 35 determines thatthe specific evaluation is not a negative evaluation or the languageevaluation is not a positive evaluation in step S61, that is, when boththe specific evaluation and the language evaluation are positiveevaluations or both of them are negative evaluations, the procedureends.

Here, when an evaluation value input by the user is greater than three,the specific-evaluation information acquisition unit 11 may determinethat the specific evaluation is a positive evaluation. When theevaluation value input by the user is three, the specific-evaluationinformation acquisition unit 11 may determine that the specificevaluation is neither a positive nor negative evaluation. When theevaluation value input by the user is less than three, thespecific-evaluation information acquisition unit 11 may determine thatthe specific evaluation is a negative evaluation. In this case, when thespecific evaluation is neither a positive nor negative evaluation,processing similar to that performed when the specific evaluation is apositive evaluation is performed. That is, when the language evaluationis a negative evaluation, the degree of similarity is calculated byusing feature values other than the language-evaluation feature value asthe subject feature values, and when the language evaluation is neithera positive nor negative evaluation, the degree of similarity is notcalculated.

As described above, the information processing apparatus 30 determinesthe subject feature value in accordance with the language-evaluationfeature value, the specific evaluation, and the language evaluation, andrecommends recommendation-candidate content items as similar contentitems in accordance with the degree of similarity regarding the subjectfeature value or values between the subject content item and each of therecommendation-candidate content items. Thus, the information processingapparatus 30 can recommend content items that match the user'spreference with higher accuracy than a case in which content items arerecommended in accordance with one of the specific evaluation and thelanguage evaluation.

Third Embodiment

FIG. 9 is a block diagram of an exemplary structure of an informationprocessing apparatus according to a third embodiment of the presentinvention.

In the structure shown in FIG. 9, components the same as those indicatedin the structure shown in FIG. 1 or 5 will be denoted by the samereference numerals. Redundant explanation will be omitted as necessary.

The structure of an information processing apparatus 50 shown in FIG. 9and the structure shown in FIG. 5 differ in that the informationprocessing apparatus 50 includes the recommendation-and-presentationunit 19 and a preference learning unit 51 instead of therecommendation-and-presentation unit 35 and the preference learning unit36.

The information processing apparatus 50 determines a learning method inaccordance with a specific evaluation, a language evaluation, andfeature values of content items corresponding to evaluation attributesof the language evaluation, and performs preference learning by thelearning method. Then, the information processing apparatus 50 presentsrecommended content items to the user in accordance with preferenceinformation created by the preference learning.

More specifically, similarly to the main-evaluation informationdetermination unit 15 shown in FIG. 1, the preference learning unit 51determines that the main evaluation is to be the specific evaluationfrom among the specific evaluation and the language evaluation.Moreover, similarly to the evaluation correction unit 16 shown in FIG.1, the preference learning unit 51 corrects the evaluation value of thespecific evaluation supplied from the specific-evaluation informationacquisition unit 11 in accordance with the relationship between thespecific evaluation and the language evaluation recognized by thespecific-evaluation-and-language-evaluation comparing unit 32, andtreats the corrected specific evaluation as a final evaluation value.

Moreover, the preference learning unit 51 determines a preferencelearning method in accordance with the specific evaluation and languageevaluation output from the specific-evaluation-and-language-evaluationcomparing unit 32 and the language-evaluation feature value determinedby the language-evaluation feature-value determination unit 34. Here,the preference learning method performed by the preference learning unit51 is a method based on the above-described second method.

The preference learning unit 51 performs preference learning by thedetermined preference learning method in accordance with the finalevaluation value and feature values of the subject content item storedin the content-feature-value storage unit 18. The preference learningunit 51 supplies, as preference information, a preference vector createdby performing preference learning to the recommendation-and-presentationunit 19.

FIG. 10 is a flowchart illustrating learning processing performed by theinformation processing apparatus 50 shown in FIG. 9. This learningprocessing starts when, for example, the user inputs a class of thefive-class ordinal scale and an evaluation sentence regarding thesubject content item.

Processing in steps S71 to S76 shown in FIG. 10 is similar to processingin steps S11 to S16 shown in FIG. 3. Thus, the description thereof isomitted.

After processing in step S76 is performed, in step S77, thelanguage-evaluation feature-value determination unit 34 determines thatthe language-evaluation feature value is to be a feature valuecorresponding to an evaluation attribute, which is a word similar to theevaluation attribute extracted by the language-evaluation informationextraction unit 31, from among the evaluation attributes in theattribute-and-feature-value correspondence information stored in theattribute-and-feature-value correspondence storage unit 33. Then, thelanguage-evaluation feature-value determination unit 34 supplies thelanguage-evaluation feature value to the preference learning unit 51.

In step S78, the preference learning unit 51 determines whether thespecific evaluation is a positive evaluation in accordance with thespecific evaluation and language evaluation supplied from thespecific-evaluation-and-language-evaluation comparing unit 32. If thepreference learning unit 51 determines that the specific evaluation is apositive evaluation in step S78, in step S79, the preference learningunit 51 determines whether the language evaluation is a positiveevaluation in accordance with the specific evaluation and languageevaluation supplied from the specific-evaluation-and-language-evaluationcomparing unit 32.

If the preference learning unit 51 determines that the languageevaluation is a positive evaluation in step S79, that is, when thespecific evaluation and the language evaluation are positiveevaluations, the procedure proceeds to step S80. In step S80, thepreference learning unit 51 determines that the preference learningmethod is to be a language-evaluation feature-value weighting method inwhich a preference vector is updated by weighting thelanguage-evaluation feature value of the subject content item.

The following will describe the language-evaluation feature-valueweighting method.

When a preference vector before learning is m=(m₁, m₂, . . . , m_(M))(note that M represents the number of types of feature value), thenumber of content items in a content group used to create the preferencevector is N_(p), and feature values of a subject content item j aref_(j)=(f_(j1), f_(j2), . . . , f_(jM)), the preference vector is updatedin accordance with Eq. (1) given below in the second method.

m′ _(i)=(N _(p) m _(i) +f _(ji))/(N _(p)+1)  Eq. (1)

Here, in Eq. (1), m′_(i) represents each element of a preference vectorm′, which is obtained as a result of learning.

Moreover, the number of content items N_(p)′ in the content group usedto create the preference vector m′ is expressed by Eq. (2) given below.

N _(p) ′=N _(p)+1  Eq. (2)

In contrast, in the language-evaluation-feature-value weighting method,a language-evaluation feature value m_(k) is updated in accordance withEq. (3) given below, and feature values m_(i) other than thelanguage-evaluation feature value m_(k) are updated in accordance withEq. (1) given above.

m′ _(k)=(N _(p) m _(k) +βf _(jk))/(N _(p)+β)  Eq. (3)

Here, in Eq. (3), a constant β is a value greater than one. According toEq. (3), a language-evaluation feature value m′_(k) of the preferencevector m′ after learning becomes closer to a language-evaluation featurevalue f_(jk) of the subject content item j. Thus, in thelanguage-evaluation-feature-value weighting method, learning isperformed by giving a heavier weight to the language-evaluation featurevalue of the preference vector. After processing in step S80 isperformed, the procedure proceeds to step S84.

In contrast, if the preference learning unit 51 determines that thelanguage evaluation is not a positive evaluation in step S79, that is,when the specific evaluation is a positive evaluation and the languageevaluation is a negative evaluation, the procedure proceeds to step S81.In step S81, the preference learning unit 51 determines that thepreference learning method is to be anexcept-for-language-evaluation-feature-value updating method in whichfeature values of the preference vector other than thelanguage-evaluation feature value of the preference vector are updatedin accordance with Eq. (1). In thisexcept-for-language-evaluation-feature-value updating method, featurevalues m′_(i) of the preference vector m′ other than thelanguage-evaluation feature value m′_(k) of the preference vector m′become closer to a feature value f_(ji) of the subject content item j.After processing in step S81 is performed, the procedure proceeds tostep S84.

Moreover, if the preference learning unit 51 determines that thespecific evaluation is not a positive evaluation in step S78, that is,when the specific evaluation is a negative evaluation, the procedureproceeds to step S82. In step S82, the preference learning unit 51determines whether the language evaluation is a positive evaluation.

If the preference learning unit 51 determines that the languageevaluation is a positive evaluation in step S82, that is, when thespecific evaluation is a negative evaluation and the language evaluationis a positive evaluation, the procedure proceeds to step S83.

In step S83, the preference learning unit 51 determines that thepreference learning method is to be anonly-language-evaluation-feature-value updating method in which only thelanguage-evaluation feature value is updated in accordance with Eq. (1).In this only-language-evaluation-feature-value updating method, only thelanguage-evaluation feature value m′_(k) of the preference vector m′becomes closer to a feature value f_(jk) of the subject content item j.After processing in step S83 is performed, the procedure proceeds tostep S84.

In step S84, the preference learning unit 51 performs preferencelearning by one of the preference learning methods determined in one ofsteps S80 to S83, and the procedure ends.

In contrast, if the preference learning unit 51 determines that thelanguage evaluation is not a positive evaluation in step S82, that is,when both the specific evaluation and the language evaluation arenegative evaluations, the procedure ends.

Here, in a case in which the preference learning unit 51 also learns anegative preference vector, when both the specific evaluation and thelanguage evaluation are negative evaluations, the preference learningunit 51 updates a language-evaluation feature value of a negativepreference vector in accordance with an equation similar to Eq. (3) andupdates feature values other than the language-evaluation feature valuein accordance with an equation similar to Eq. (1). As a result, learningis performed by giving a heavier weight to the language-evaluationfeature value of the negative preference vector.

Recommended-content presentation processing performed by the informationprocessing apparatus 50 is similar to the recommended-contentpresentation processing shown in FIG. 4. Thus, the description thereofis omitted.

As described above, the information processing apparatus 50 determinesthe preference learning method in accordance with a language-evaluationfeature value, a specific evaluation, and a language evaluation, andrecommends content items in accordance with preference informationcreated by preference learning performed by the preference learningmethod. Thus, the information processing apparatus 50 can recommendcontent items that match the user's preference with higher accuracy thana case in which content items are recommended in accordance with one ofthe specific evaluation and the language evaluation.

Here, in the above-described description, the evaluation value used inpreference learning is the corrected evaluation value of the mainevaluation in the information processing apparatuses 30 and 50, thecorrection being performed in accordance with the relationship betweenthe specific evaluation and the language evaluation. However, theevaluation value used in preference learning may be the evaluation valueof the specific evaluation or the evaluation value of the languageevaluation as it is.

Moreover, embodiments according to the present invention can also beapplied to a recording apparatus that records recommended content itemsor similar content items as well as an apparatus that presentscontent-related information items regarding the recommended contentitems or similar content items.

The above-described series of processing processes can be executed byhardware or software. When the series of processing processes isexecuted by software, a program constituting the software is installedon a computer. Here, examples of the computer include a computerembedded in dedicated hardware, a general-purpose personal computer thatis capable of performing various functions with installed variousprograms, and the like.

FIG. 11 is a block diagram of an example of the structure of hardware ofa computer that executes the above-described series of processingprocesses by using a program.

In the computer, a central processing unit (CPU) 201, a read-only memory(ROM) 202, and a random access memory (RAM) 203 are connected to eachother via a bus 204.

Furthermore, an input/output interface 205 is connected to the bus 204.An input unit 206, an output unit 207, a storage unit 208, acommunication unit 209, and a drive 210 are connected to theinput/output interface 205.

The input unit 206 includes a keyboard, a mouse, and a microphone. Theoutput unit 207 includes a display and a speaker. The storage unit 208includes a hard disk and a nonvolatile memory. The communication unit209 includes a network interface. The drive 210 drives a removablemedium 211 such as a magnetic disk, an optical disk, a magneto-opticaldisk, or a semiconductor memory.

In the computer having the above-described structure, theabove-described series of processing processes is performed when, forexample, the CPU 201 loads the program stored in the storage unit 208into the RAM 203 via the input/output interface 205 and the bus 204 andexecutes the program.

The program executed by the computer (the CPU 201) may be provided by,for example, the removable medium 211, which is a packaged medium or thelike, on which the program is recorded. Moreover, the program may beprovided via a wired or wireless transmission medium such as a localarea network, the Internet, or digital satellite broadcasting.

In the computer, the program can be installed on the storage unit 208via the input/output interface 205 by inserting the removable medium 211in the drive 210. Moreover, the program can be received by thecommunication unit 209 via a wired or wireless transmission medium andcan be installed on the storage unit 208. Alternatively, the program canbe preinstalled on the ROM 202 or the storage unit 208.

In the specification, the steps described in the program recorded on aprogram recording medium may be, as a matter of course, performed inaccordance with the time sequence following the described order. Thesteps does not have to be performed in accordance with the time sequencefollowing the described order and may be performed in parallel orindividually.

The present application contains subject matter related to thatdisclosed in Japanese Priority Patent Application JP 2009-187046 filedin the Japan Patent Office on Aug. 12, 2009, the entire content of whichis hereby incorporated by reference.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

1-12. (canceled)
 13. An information processing apparatus, comprising: acircuitry configured to: receive voice data of a user, the voice datarelated to an item; convert the voice data into language text data;analyze the language text data to determine preference of the user forthe item; accumulate preferences of the user for items; and extract atleast one recommendation item based on the preferences.
 14. Theinformation processing apparatus according to claim 13, wherein thecircuitry receives rating for an item selected by a client terminal ofthe user among the plurality of ratings; and wherein the circuitryaccumulates the preferences by taking the received rating intoconsideration.
 15. The information processing apparatus according toclaim 13, wherein the circuitry is further configured to: determinewhich of the preferences is to be the main preference, wherein thecircuitry learns the preference based on at least one of the languagetext data or the accumulated preferences.
 16. The information processingapparatus according to claim 13, wherein the circuitry determines acontent feature value, which is to be used to calculate a degree ofsimilarity or a distance between the recommendation item and arecommendation-candidate item, and wherein the circuitry recommends therecommendation-candidate item in accordance with the degree ofsimilarity or the distance between the recommendation item and therecommendation-candidate content item.
 17. The information processingapparatus according to claim 13, wherein the circuitry is furtherconfigured to: learn the preference by using at least one of thelanguage text data or the accumulated preferences, and a feature valueof the item, wherein the circuitry also acquires an attribute of thelanguage evaluation from the language text data, and wherein thecircuitry determines a learning method in accordance with a contentfeature value corresponding to the attribute, and learns the preferenceby the learning method.
 18. The information processing apparatusaccording to claim 17, wherein the circuitry determines that thelearning method is to be a method in which the preference is learnt byweighting a feature value of the recommendation item, the feature valuecorresponding to the attribute.
 19. The information processing apparatusaccording to claim 17, determines that the learning method is to be amethod in which the preference is learnt by using only a feature valueof the recommended item, the feature value being other than the featurevalue corresponding to the attribute.
 20. A method for processinginformation performed by an information processing apparatus, the methodcomprising the steps of: receiving voice data from a user, the voicedata related to an item; converting the voice data into language textdata; analyzing the language text data to determine preference of theuser for the item; accumulating preferences of the user for items; andextracting at least one recommendation item based on the preferences.21. A non-transitory computer readable storage medium storing a computerprogram for causing a computer to execute processing including the stepsof: receiving voice data from a user, the voice data related to an item;converting the voice data into language text data; analyzing thelanguage text data to determine preference of the user for the item;accumulating preferences of the user for items; and extracting at leastone recommendation item based on the preferences.